Real-time modelling and forecasting
during infectious disease outbreaks


Sebastian Funk
22 March, 2018
recon gathering, London

Summer 2014

\begin{eqnarray} \dot{S}&=&-\beta \frac{S}{N}I\\ \dot{I}&=&+\beta \frac{S}{N}I - \gamma I\\ \dot{R}&=&+\gamma I \end{eqnarray}

"[…], Liberia and Sierra Leone will have approximately 550,000 Ebola cases (1.4 million when corrected for underreporting)"

Meltzer, 2014

What really happened

"[…], Liberia and Sierra Leone will have approximately 550,000 Ebola cases (1.4 million when corrected for underreporting)"

Meltzer, 2014

"without additional interventions or changes in community behavior (e.g., notable reductions in unsafe burial practices), the model also estimates that Liberia and Sierra Leone will have approximately 550,000 Ebola cases (1.4 million…)"

Meltzer, 2014

"A CDC model […] was key to increasing the speed and scale of the US and global response.

Frieden, 2015

Key findings:

  1. "cases were increasing exponentially, and the response needed was massive and urgent"
  2. "the model predicted a severe penalty for delay"
  3. "the model identified a tipping point at which the epidemic would [..] decline if enough Ebola patients were isolated effectively and decedents buried safely"
  4. "the model predicted that when the tipping point was reached, transmission would decline rapidly"

Samuel V. Scarpino @svscarpino

Meaningful forecasts are probabilistic.

Uses of real-time forecasts in outbreaks

  • Plan the scale of a response or intervention
  • Allocate resources (e.g., geographically)
  • Plan clinical trials

Challenges/opportunities

  1. Evaluation of probabilistic forecasts

Evaluating probabilistic forecasts requires
multiple observations.

1-week forecasts

Calibration: Compatibility of forecasts and observations.

Calibration: Compatibility of forecasts and observations.

Calibration: Compatibility of forecasts and observations.

Sharpness

  1. Integration of different data sources

Improve forecasts by
all available data streams
(individual/behavioural/spatial/genetic)?

Louis du Plessis, University of Oxford (unpublished)

New tools

New tools

  1. Forecasting for decision making

Acknowledgements

Anton Camacho, Adam Kucharski, John Edmunds, Rachel Lowe,
Roz Eggo (LSHTM), Louis du Plessis (Oxford),
Tilmann Gneiting (Heidelberg), James Hensman (prowler.io),
Lawrence Murray (Uppsala)

Summary

  • Real-time forecasts can aid decision making
  • Meaningful forecasts are probabilistic
  • Forecasts must be evaluated to establish reliability and limitations
  • Some big challenges remain


Assessing the performance of real-time epidemic forecasts
S.F., A. Camacho, A. J. Kucharski, R. Lowe, R. M. Eggo, W. J. Edmunds
bioRxiv 177451; doi: https://doi.org/10.1101/177451